Yu and Koltun suggest in [1]} that standard the task of dense predictions is structurally different from image classification. They show that dilated convolutions perform better than standard CNNs on dense prediction tasks. In [2]} Yu et al. introduce Dilated Residual Networks, which are a modification of standard ResNets[3]} using dilated convolutions instead of downsampling of feature maps. This architecture achieved much better performance on tasks like image segmentation and object localization. We use the idea proposed in this paper and adapt it to the few shot setting.
